Exercise

# The Regression Coefficients II

With both `smile`

and `money`

in your model, you found that the slope coefficient for `money`

is 0.8008, `smile`

is 1.4895 and the intercept is 0.6162. How is this different from predicting `liking`

with **only** `smile`

or **only** `money`

?

In your console, look at the coefficients for a model with only `smile`

by entering `lm(liking ~ smile)`

and only `money`

by entering `lm(liking ~ money)`

. Compare the change in coefficients from the model with two predictors, to the model with one predictor and select the correct statment from below.

Instructions

### Possible answers

Both money and smiling are stronger predictors of liking when they are included in the model together.

Both money and smiling are stronger predictors of liking individually in the one-predictor models, compared to in the model with two predictors.

Money is a stronger predictor in the model with two predictors, smiling is stronger in the model with one predictor.

While the intercept stays at the same value, both predictors change in the model containing two predictors compared to the models with one predictor.